About this book

This book is full version of our research paper [Please add paper publish information here].

Keywords deep learning; single-cell RNA-seq; imputation; dimention reduction; clustering; batch correction; cell type identification; functional prediction; visualization

Abstract

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. However, together with the growth of single-cell profiling technologies, there has also been an increase of computational challenges to process the generated datasets. It’s here that by effectively leveraging large data sets, Deep Learning (DL) is positioning as the first option for single-cell analyses. Here we provide a unified mathematical description of the DL methods used in single cell RNA sequencing (scRNA-Seq) followed with the survey of the most representative published DL algorithms for scRNA-Seq in the field.

Key Points

• Single cell RNA sequencing technology generate large collection of transcriptomic profiles of up to millions of cells, enabling biological investigation of hidden structures or cell types, predicting their effects or responses to treatment more precisely, or utilizing sub-population to address unanswered hypotheses.
• Current Deep Learning-based analysis approaches for single cell RNA seq data is systematically reviewed in this paper according to the challenge they address and their roles in the analysis pipeline.
• A unified mathematical description of the surveyed DL models is presented and the specific model features were discussed when reviewing each approach.
  
• A comprehensive summary of the evaluation metrics, comparison algorithms, and datasets by each approaches is presented.